pineapple3.ipynb

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    "\"\"\"Demonstrates how to make a simple call to the Natural Language API.\"\"\"\n",
    "\n",
    "import argparse\n",
    "\n",
    "from google.cloud import language\n",
    "from google.cloud.language import enums\n",
    "from google.cloud.language import types"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We import argparse, a standard library, to allow the application to accept input filenames as arguments.\n",
    "\n",
    "For using the Cloud Natural Language API, we'll also want to import the language module from the google-cloud-language library. The types module contains classes that are required for creating requests."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_result(annotations):\n",
    "    score = annotations.document_sentiment.score\n",
    "    magnitude = annotations.document_sentiment.magnitude\n",
    "\n",
    "    for index, sentence in enumerate(annotations.sentences):\n",
    "        sentence_sentiment = sentence.sentiment.score\n",
    "        print('Sentence {} has a sentiment score of {}'.format(\n",
    "            index, sentence_sentiment))\n",
    "\n",
    "    print('Overall Sentiment: score of {} with magnitude of {}'.format(\n",
    "        score, magnitude))\n",
    "    return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def analyze(movie_review_filename):\n",
    "    \"\"\"Run a sentiment analysis request on text within a passed filename.\"\"\"\n",
    "    client = language.LanguageServiceClient()\n",
    "\n",
    "    with open(movie_review_filename, 'r') as review_file:\n",
    "        # Instantiates a plain text document.\n",
    "        content = review_file.read()\n",
    "\n",
    "    document = types.Document(\n",
    "        content=content,\n",
    "        type=enums.Document.Type.PLAIN_TEXT)\n",
    "    annotations = client.analyze_sentiment(document=document)\n",
    "\n",
    "    # Print the results\n",
    "    print_result(annotations)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "usage: ipykernel_launcher.py [-h] movie_review_filename\n",
      "ipykernel_launcher.py: error: unrecognized arguments: -f\n"
     ]
    },
    {
     "ename": "SystemExit",
     "evalue": "2",
     "output_type": "error",
     "traceback": [
      "An exception has occurred, use %tb to see the full traceback.\n",
      "\u001b[0;31mSystemExit\u001b[0m\u001b[0;31m:\u001b[0m 2\n"
     ]
    }
   ],
   "source": [
    "if __name__ == '__main__':\n",
    "    parser = argparse.ArgumentParser(\n",
    "        description=__doc__,\n",
    "        formatter_class=argparse.RawDescriptionHelpFormatter)\n",
    "    parser.add_argument(\n",
    "        'movie_review_filename',\n",
    "        help='The filename of the movie review you\\'d like to analyze.')\n",
    "    args = parser.parse_args()\n",
    "\n",
    "    analyze(args.movie_review_filename)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we simply parse the passed argument for the text filename and pass it to the analyze() function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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